Maikel Leon | Artificial Intelligence | Research Excellence Award

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Assoc. Prof. Dr. Maikel Leon | Artificial Intelligence | Research Excellence Award

University of Miami, United States

Assoc. Prof. Dr. Maikel Leon is an accomplished academic and AI specialist with a Ph.D. in Computer Science focused on artificial intelligence applied to transportation from Hasselt University, Belgium, and summa cum laude degrees from the Central University of Las Villas, Cuba. Since 2015, he has been a faculty member at the Department of Business Technology, Miami Herbert Business School, University of Miami, teaching and coordinating a wide range of courses in business analytics, programming, machine learning, databases, and artificial intelligence for business. His academic career spans institutions in the United States and Cuba, reflecting strong international teaching and research experience. Dr. Leon is an active reviewer and program committee member for leading journals and conferences, including IEEE Transactions on Fuzzy Systems and FLAIRS. He has received prestigious honors such as the Best Paper Award at the IEEE ICTAI Conference and the Cuban National Academy of Sciences Award for outstanding research. Beyond academia, he is a frequent media commentator on AI, a certified professional in generative AI and cloud technologies, and a leader in innovative teaching, entrepreneurship, and international collaboration initiatives.

Citation Metrics (Scopus)

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Featured Publications

 

Shengchao Liu | Computer Science | Research Excellence Award

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Dr. Shengchao Liu | Computer Science | Research Excellence Award

The Chinese University | Hong Kong

Shengchao Liu is a tenure-track Assistant Professor in the Department of Computer Science and Engineering at The Chinese University of Hong Kong, whose research lies at the intersection of machine learning, geometry, and scientific discovery. His work focuses on developing foundation models and physics-inspired learning frameworks for molecules, proteins, and materials, with the long-term goal of accelerating discovery in chemistry, biology, and materials science. By integrating multi-modal data, symmetry principles, and domain knowledge, his research bridges theoretical advances in AI with real-world experimental impact. A central theme of Dr. Liu’s research is geometric and symmetry-informed representation learning. He has pioneered group-equivariant and manifold-constrained generative models that respect the underlying physical laws of molecular and material systems. His contributions include SE(3)-invariant pretraining methods, group-symmetric stochastic differential equation models, and rigid flow matching techniques, which have significantly improved the fidelity and interpretability of molecular generation and dynamics modeling. These methods form a unifying framework for learning across molecules, proteins, and crystalline materials, as demonstrated in his influential works at ICLR, ICML, NeurIPS, and AISTATS. Dr. Liu’s work is deeply collaborative and interdisciplinary. He has worked closely with leading researchers across academia and industry, including Mila, UC Berkeley, NVIDIA Research, and national laboratories. As a Principal Investigator, he has led NERSC-supported projects on foundation models for material discovery, leveraging large-scale GPU resources to push the frontier of generative AI for science. His research has also contributed widely used open-source resources, including geometric graph learning benchmarks and toolkits adopted by the broader AI-for-science community.

Citation Metrics (Google Scholar)

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Featured Publications


Pre-training Molecular Graph Representation with 3D Geometry

– International Conference on Learning Representations , 2021 | Cited by 574


N-Gram Graph: Simple Unsupervised Representation for Graphs, with Applications to Molecules

– Advances in Neural Information Processing Systems, 2019 | Cited by 295


A text-guided protein design framework

– Nature Machine Intelligence, 2025 | Cited by 225

 

Christos Bouras | Computer Science | Research Excellence Award

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Prof. Christos Bouras | Computer Science | Research Excellence Award

Prof. Christos Bouras | University of Patras | Greece

Professor Christos Bouras is a distinguished academic leader and renowned computer engineering expert, currently serving as Professor in the Department of Computer Engineering and Informatics and Rector of the University of Patras, Greece. He holds a Diploma and a PhD in Computer Engineering and Informatics from the University of Patras. Over the course of his career, he has made substantial contributions to advanced networking technologies, digital communications, and distributed systems while leading major academic, administrative, and international initiatives. His research expertise spans and Beyond Networks, performance analysis of networking and computer systems, mobile and wireless communications, telematics, QoS and pricing mechanisms, e-learning technologies, and networked virtual environments. As an active member of IEEE and ACM, Professor Bouras has built a global reputation for innovative contributions and collaborative research. He has also held several prestigious roles, including Honorary Professor at the College of Information Engineering, Sichuan Agricultural University, China, and President of the University of Patras Property Utilization & Management Company. His long-standing academic leadership is matched by a major international presence in scholarly events. Professor Bouras has participated extensively in international conference committees for more than three decades, contributing to global research dialogue in computing, networking, and educational technologies. His committee roles span top-tier conferences such as ACM STOC, ICALP, IEEE ICALT, ICL, ICWN, ICOMP, GRID Computing, and numerous specialized workshops across Europe, Asia, and North America. His involvement includes organizing committees, program committees, keynote speaking, and advisory roles in areas such as distributed algorithms, multimedia systems, web-based learning, virtual environments, mobile technologies, simulation and modeling, and entertainment computing. Widely respected for his research excellence, international collaboration, and academic leadership, Professor Bouras continues to advance global innovation in computer networks, digital systems, and technology-enhanced learning.

Profiles: Google Scholar

Featured Publications

Jurgelionis, A., Fechteler, P., Eisert, P., Bellotti, F., David, H., Laulajainen, J. P., Bouras, C., … (2009). Platform for distributed 3D gaming. International Journal of Computer Games Technology, 2009(1), Article 231863.

Bouras, C., Kollia, A., & Papazois, A. (2017). SDN & NFV in 5G: Advancements and challenges. In 2017 20th Conference on Innovations in Clouds, Internet and Networks (ICIN) (pp. xx–xx). IEEE.

Bouras, C., & Tsogkas, V. (2012). A clustering technique for news articles using WordNet. Knowledge-Based Systems, 36, 115–128.

Bouras, C., & Tsiatsos, T. (2006). Educational virtual environments: Design rationale and architecture. Multimedia Tools and Applications, 29(2), 153–173.

Bouras, C., Philopoulos, A., & Tsiatsos, T. (2001). e-Learning through distributed virtual environments. Journal of Network and Computer Applications, 24(3), 175–199.

Bouras, C., Ntarzanos, P., & Papazois, A. (2016). Cost modeling for SDN/NFV based mobile 5G networks. In 2016 8th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT) (pp. xx–xx). IEEE.

Bouras, C., Konidaris, A., & Kostoulas, D. (2004). Predictive prefetching on the web and its potential impact in the wide area. World Wide Web, 7(2), 143–179.

Jinkai Zheng | Computer Vision | Best Researcher Award 

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Prof. Jinkai Zheng | Computer Vision | Best Researcher Award 

Hangzhou Dianzi University, China

Prof. Jinkai Zheng is a Distinguished Associate Researcher at Hangzhou Dianzi University, Director of the Scientific Research Management Department at the Hangzhou Dianzi University Lishui Research Institute, and an active member of the Multimedia and Biometric Recognition Professional Committees of the China Society of Image and Graphics. His research focuses on artificial intelligence, computer vision, and multimedia analysis, with a particular emphasis on gait recognition and human-centered intelligent analysis. He has published over 40 academic documents, including multiple first-author and corresponding-author papers in top-tier venues such as CVPR, ACM Multimedia, and IEEE Transactions on Multimedia, accumulating more than 900 citations with an h-index of 16 (Google Scholar, 2025). His contributions include the Gait3D dataset, now a widely adopted benchmark by over 300 prestigious institutions worldwide, including Columbia University, University of Pennsylvania, Johns Hopkins University, and NUS. He has received notable accolades, such as the Special Prize of the 2024 Wu Wenjun Artificial Intelligence Science and Technology Progress Award, the Outstanding Paper Award at the 2023 CSIG Youth Scientists Conference, and the Best Paper Award-Honorable Mention at IEEE ISCAS 2021. With four authorized invention patents, long-term service as a reviewer for leading journals and conferences, and significant participation in national R&D projects, Prof. Zheng has become a recognized young leader in advancing AI-driven multimedia understanding.

Profiles: Scopus | Orcid | Google Scholar

Featured Publications

Zheng, J., Liu, X., Gu, X., Sun, Y., Gan, C., Zhang, J., Liu, W., & Yan, C. (2022). Gait recognition in the wild with multi-hop temporal switch. Proceedings of the 30th ACM International Conference on Multimedia, 6136–6145.

Zheng, J., Liu, X., Wang, S., Wang, L., Yan, C., & Liu, W. (2023). Parsing is all you need for accurate gait recognition in the wild. Proceedings of the 31st ACM International Conference on Multimedia, 116–124.

Zheng, J., Liu, X., Yan, C., Zhang, J., Liu, W., Zhang, X., & Mei, T. (2021). Trand: Transferable neighborhood discovery for unsupervised cross-domain gait recognition. 2021 IEEE International Symposium on Circuits and Systems (ISCAS), 1–5. IEEE.

Zheng, J., Liu, X., Zhang, B., Yan, C., Zhang, J., Liu, W., & Zhang, Y. (2024). It takes two: Accurate gait recognition in the wild via cross-granularity alignment. Proceedings of the 32nd ACM International Conference on Multimedia, 8786–8794.

Yuan, S., Zheng, J., Li, X., Sun, Y., Li, W., Gao, R., Omar, M. H., & Zhang, J. (2025). Noisy label learning for gait recognition in the wild. Electronics, 14(19), 3752.

Zhang, S., Zheng, J., Zhu, S., & Yan, C. (2025). TrackletGait: A robust framework for gait recognition in the wild. arXiv preprint arXiv:2508.02143.

Zheng, J., Liu, X., Liu, W., He, L., Yan, C., & Mei, T. (n.d.). Supplementary material for “Gait recognition in the wild with dense 3D representations and a benchmark.”

Kangwon Lee | Computer Science | Best Researcher Award

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Mr. Kangwon Lee | Computer Science | Best Researcher Award

Gyeongsang National University | South Korea

Mr. kangwon lee is a senior undergraduate student in Computer Engineering at Gyeongsang National University, specializing in artificial intelligence, music technology, audio signal processing, and natural language processing. he has pursued impactful research projects, including the development of an AI-based sentiment analysis and depression risk detection platform that integrates Valence, Arousal, and Dominance (VAD) metrics for more nuanced prediction models. As a co-author, he contributed to a peer-reviewed paper on AI-based emotion detection and expert-linked platforms, published in the Journal of the Korea Information Technology Society (2025), and his work earned the Excellence Prize at the 33rd Software Contest hosted by Gyeongsang National University in 2024. Professionally, Mr. lee has demonstrated strong technical and problem-solving skills across both civilian and military roles. At Appen Limited, he currently works as a Quality Assurance specialist, where he ensures data quality and optimizes annotation processes. During his military service, he served as an RPA Developer and Convergence Systems Developer for the Republic of Korea Air Force, achieving major efficiency gains by enhancing automation workflows with Python and UiPath. Additionally, he gained hands-on IT support experience at Samdong Heungsan Co., Ltd., managing system maintenance, software deployment, and network configuration for DB Insurance. Proficient in Python, PyTorch, SQL, and UiPath, Mr. lee holds certifications such as SQLD and TOEIC Speaking (AL). His career reflects a strong commitment to integrating AI technologies into human-centered applications, advancing innovative solutions that bridge technical advancement with social impact.

Profile: Orcid 

Featured Publications

Web-Based Platform for Quantitative Depression Risk Prediction via VAD Regression on Korean Text and Multi-Anchor Distance Scoring

Development of an AI-based Sentiment Analysis and Expert-Linked Platform for Early Detection of Socially Isolated and Depression Risk Groups

Hussein Alabdally | Computer Science | Best Researcher Award

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Mr. Hussein Alabdally | Computer Science | Best Researcher Award

Mr. Hussein Alabdally | University of Southern Queensland | Australia

Mr. Hussein Alabdally is a talented computer scientist, software engineer, and telecommunications specialist with diverse professional expertise spanning Australia and Iraq. With a foundation in mathematics, web development, and programming, he has contributed significantly to education, technology, and translation services. Hussein’s journey reflects his adaptability and passion for learning, from tutoring students in mathematics and English to working in IT, telecommunications, and software engineering roles. His bilingual communication skills in English and Arabic have enabled him to serve communities as an interpreter and translator, while his technical creativity continues to drive his work in coding, software design, and network systems.

Profiles

Scopus
Google Scholar

Education

Mr. Hussein’s educational path is marked by academic excellence in mathematics, computer science, and engineering studies. He earned his Bachelor of Science degree in Toowoomba, Australia, achieving outstanding results in advanced courses including operations research, numerical computing, experimental design, and web technologies. His solid foundation in mathematics and computing equipped him with analytical and problem-solving skills crucial for tackling real-world technical challenges. Alongside formal studies, he pursued professional training in web development and programming, mastering coding languages such as HTML, CSS, Python, JavaScript, and C++. Hussein also gained practical experience in website design and database management, complementing his academic knowledge with hands-on projects.

Experience

Mr. Hussein’s professional experience covers a wide range of roles across education, IT, translation, and engineering. He worked as a website developer with leading companies in Toowoomba, building digital platforms and enhancing user experience. His teaching journey as an English and mathematics tutor demonstrated his ability to simplify complex concepts for students, helping many succeed in academic pursuits. Hussein’s bilingual expertise was recognized in his work as an interpreter, supporting communication in medical, legal, and educational contexts. Transitioning into engineering roles, he contributed as an IT specialist at Dar Al-Auloom Private High School and later advanced to software engineering and telecommunications positions in Kirkuk. His diverse portfolio reflects both technical mastery and cultural adaptability.

Research Interests

Mr. Hussein’s research interests are deeply rooted in the intersection of mathematics, programming, and technology innovation. He is passionate about computational methods, web technologies, and advanced applications of mathematical modeling in computer engineering. His curiosity extends to artificial intelligence, game programming, and database systems, where he enjoys creating applications that merge creativity with technical precision. Hussein is particularly enthusiastic about designing intelligent software solutions, including document readers and chess games with AI capabilities. He also explores optimization techniques and performance computing, driven by a desire to apply theoretical knowledge to practical systems. His long-term vision is to bridge mathematics with next-generation software solutions.

Awards

Mr. Hussein’s achievements highlight his academic dedication and community engagement. He earned recognition in national and international competitions, including the Australian Statistics Competition, where he won the Queensland prize. He also secured credits in the UNSW ICAS Science and Mathematics contests, demonstrating excellence across STEM disciplines. At the University of Southern Queensland, he was actively involved in science and engineering challenges, achieving commendable rankings. Beyond academics, Hussein received awards for both academic excellence and school community participation, showcasing his commitment to leadership and service. These honors underline his consistent performance, strong analytical skills, and ability to contribute meaningfully both inside and outside the classroom.

Publication Top Notes

Empirical curvelet transform based deep DenseNet model to predict NDVI using RGB drone imagery data
Journal: Computers and Electronics in Agriculture, 
Authors: M. Diykh, M. Ali, M. Jamei, S. Abdulla, M.P. Uddin, A.A. Farooque, A.H. Labban, H. Alabdally, et al.

Improving Dry-Bulb Air Temperature Prediction Using a Hybrid Model Integrating Genetic Algorithms with a Fourier–Bessel Series Expansion-Based LSTM Model
Journal: Forecasting, 
Authors: H. Alabdally, M. Ali, M. Diykh, R.C. Deo, A.A. Aldhafeeri, S. Abdulla, et al.

ECT-DLM: Deep Learning Based Empirical Curvelet Transform Approach for Thoracic Disease Diagnosis from X-RAY Images
Conference: ICTIS
Authors: S. Abdulla, S.K. Alkhafaji, H. Marhoon, M. Diykh, M.A. Majed, J. Sadiq, H. Alabdally, et al.

Physical Human Activity Recognition Based on Spectral Graph Wavelet Transforms Integrated with Machine Learning Model
Conference: International Conference on Health Information Science,
Authors: S. Abdulla, A.S. Majeed, A.B. Al-Khafaji, W. Alsalman, M. Diykh, A. Sahi, H. Alabdally, et al.

Robust Approach for Human Activity Recognition Using Decomposing Technique Based Machine Learning Models
Conference: International Conference on Health Information Science,
Authors: S.Z. Hmoud, M. Diykh, S. Abdulla, H. Alabdally, A. Sahi

Conclusion

Mr. Hussein Alabdally represents a professional who blends education, technical skill, and cultural versatility. His journey reflects resilience, adaptability, and a deep passion for mathematics and technology. Whether teaching students, translating across languages, or designing digital systems, Hussein demonstrates excellence in every role he undertakes. His dual citizenship in Australia and Iraq positions him as a global professional with a multicultural perspective. With his diverse experience in tutoring, web development, software engineering, and telecommunications, Hussein continues to grow as a researcher and practitioner in the field of computer science. His career trajectory shows promise for future contributions to both academia and industry.

Dr. David Hua | Artificial Intelligence | Best Researcher Award

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Dr. David Hua | Artificial Intelligence | Best Researcher Award

Ball State University, United States.

Dr. David M. Hua is an Associate Professor at the Center for Information and Communication Sciences, Ball State University. With a rich academic background and over two decades of teaching, Dr. Hua has become a pivotal figure in the intersection of technology education, cybersecurity, and higher education. He is recognized for mentoring student-led innovation and his contribution to emerging tech curricula including offensive security, private cloud infrastructure, and sustainability in IT.

Profile

Scopus
Orcid

🎓 Education

Dr. Hua earned his Ed.D. in Higher Education in 2010 from Ball State University, where he also completed an MBA in Information Systems (2000) and a B.S. in Psychological Science (1991). This diverse academic foundation reflects his commitment to both technical expertise and educational leadership.

💼 Experience

Since July 20, 1998, Dr. Hua has served at Ball State University, advancing to the role of Associate Professor. He began as an Assistant Professor in 2000. His teaching spans undergraduate and graduate levels with courses ranging from cybersecurity and network configuration to cloud technologies. Beyond Ball State, his engagements with other institutions and organizations have broadened his interdisciplinary impact on both students and faculty.

🔬 Research Interests

Dr. Hua’s research interests lie at the crossroads of cybersecurity, AI in mental health surveillance, sustainable IT practices, and technology integration in higher education. He is especially passionate about leveraging machine learning to support mental health outcomes and empower student innovation through data-driven methodologies.

🏆 Awards & Mentorship

Dr. Hua has been an active mentor in various student projects, honors theses, and national competitions like the National Cyber League. He’s also served on several doctoral committees, contributing to dissertations in educational leadership and adult learning. His efforts have earned him recognition as a dedicated mentor, innovator, and academic leader.

📚 Publication

AI-Driven Mental Health Surveillance: Identifying Suicidal Ideation Through Machine Learning Techniques
📅 2025 | Big Data and Cognitive Computing
🧾 Cited by: 3 articles (as of early 2025)
👉 DOI: 10.3390/bdcc9010016

Prof. Dr. Saleh Albahli | Artificial Intelligence | Best Researcher Award

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Prof. Dr. Saleh Albahli | Artificial Intelligence | Best Researcher Award

Qassim University, Saudi Arabia.

Dr. Saleh Albahli is a highly accomplished academic and researcher specializing in Digital Transformation, Data Science, and Artificial Intelligence. Currently an Associate Professor and Vice-Dean of Information Technology Deanship at Qassim University, he is known for spearheading transformative digital initiatives, leading enterprise architecture projects, and contributing to cutting-edge research in machine learning and deep learning. His work is globally recognized, ranking him among the top 2% of scientists in AI research worldwide.

Profile

Scopus

Orcid

🎓 Education

Dr. Saleh Albahli holds a Ph.D. in Computer Science with distinction from Kent State University, USA (2016), showcasing his expertise in advanced computational methodologies and research excellence. He earned a Master’s degree in Information Technology with distinction from The University of Newcastle, Australia (2010), highlighting his dedication to mastering cutting-edge IT solutions. His academic journey began with a Bachelor’s degree in Computer Science from King Saud University, Saudi Arabia (2004), laying a strong foundation for his accomplished career in technology and innovation.

💼 Experience

Dr. Saleh Albahli has built an illustrious career, currently serving as an Associate Professor in the Department of IT at Qassim University since 2020, where he contributes to advancing education and research. Concurrently, he holds dual leadership roles as Vice-Dean of IT Deanship and Director of Enterprise Architecture & Digital Transformation at Qassim University, spearheading transformative initiatives to enhance technological frameworks and drive digital innovation.

Previously, Dr. Albahli gained international experience as a Senior System Analyst at Cleveland Clinic, USA (2015–2016), where he developed cutting-edge systems to optimize healthcare operations. He also served as a Lecturer at Kent State University, USA (2015–2016), imparting knowledge and fostering academic growth. Earlier in his career, he worked as an Oracle Developer and Apps DBA at Riyadh Bank and Integrated Telecom Company in Saudi Arabia (2005–2007), honing his technical expertise in database systems and enterprise applications.

🔬 Research Interests

Digital Transformation and its integration with enterprise architecture

Machine Learning and Deep Learning Pipelines

Big Data Analytics, Data Governance, and Predictive Analytics

Artificial Intelligence Applications in healthcare and business

Process Optimization in technology-driven environments

🏆 Awards & Recognitions

Ranked among the top 2% of scientists globally in AI research (2022)

First Place in Digital Transformation (Qiyas) – Qassim University (2022, 2023)

ISO certifications in 22301, 20000, and 27001 for excellence in IT management

📚 Selected Publications 

Efficient Hyperparameter Tuning for Predicting Student Performance with Bayesian Optimization
Albahli, S.
Multimedia Tools and Applications, 2024, 83(17), pp. 52711–52735.
This study introduces a Bayesian optimization approach to enhance hyperparameter tuning for predictive models in educational datasets, achieving improved accuracy and efficiency. (Citations: 4)

MedNet: Medical Deepfakes Detection Using an Improved Deep Learning Approach
Albahli, S., Nawaz, M.
Multimedia Tools and Applications, 2024, 83(16), pp. 48357–48375.
This paper presents MedNet, a novel deep learning framework tailored to detect medical deepfakes, ensuring the integrity of critical healthcare data. (Citations: 4)

Opinion Mining for Stock Trend Prediction Using Deep Learning
Albahli, S., Nazir, T.
Multimedia Tools and Applications, 2024.
Leveraging deep learning techniques, this research focuses on sentiment analysis to predict stock trends, demonstrating robust performance metrics. (Citations: 0)

An Improved DenseNet Model for Prediction of Stock Market Using Stock Technical Indicators
Albahli, S., Nazir, T., Nawaz, M., Irtaza, A.
Expert Systems with Applications, 2023, 232, 120903.
This work proposes enhancements to DenseNet architectures for stock market predictions based on technical indicators, achieving notable predictive accuracy. (Citations: 10)

A Circular Box-Based Deep Learning Model for the Identification of Signet Ring Cells from Histopathological Images
Albahli, S., Nazir, T.
Bioengineering, 2023, 10(10), 1147.
This open-access study develops a circular box-based deep learning model for the accurate detection of signet ring cells in histopathological images, aiding cancer diagnosis.

 

 

 

Mrs. Golshid Ranjbaran | Artificial Intelligence | Best Researcher Award

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Mrs. Golshid Ranjbaran | Artificial Intelligence | Best Researcher Award

University of Saskatchewan, Canada.

Golshid Ranjbaran is a PhD Candidate in Computer Science at the University of Saskatchewan (USASK), specializing in Artificial Intelligence, Machine Learning, and Interpretability. With a Bachelor's degree in Software Engineering and a Master's in Artificial Intelligence from the Science and Research Branch in Iran, he has accumulated several awards, including the Best Paper Award at the IKT Conference in 2021 and Best Researcher at ITRC in 2022. Golshid's research is aimed at advancing AI methodologies and improving machine learning models for real-world applications. He was also a research associate at the Data Science & Big Data Lab in Seville, Spain, in 2023. 🌐

Profile

Google Scholar

Education 🎓

Golshid holds a Bachelor's degree in Software Engineering and a Master's degree in Artificial Intelligence from the Science and Research Branch in Iran. He is currently pursuing a Ph.D. in Computer Science at the University of Saskatchewan (USASK), Canada, where he focuses on AI, machine learning, and interpretability, aiming to bridge the gap between theoretical advancements and practical applications.

Experience 🏢

Golshid has been awarded several prestigious positions and accolades, including a research position at the Data Science & Big Data Lab in Seville, Spain (2023), and was recognized as the Best Researcher at ITRC (2022). He has also contributed to various consultancy projects and industry collaborations, such as working on AI systems at ITRC, smart meters algorithms, and data governance in Iran.

Research Interests 🔍

Enhancing model interpretability through methods like SHAP.

Exploring sentiment analysis for stock market prediction.

Developing augmented techniques for unbalanced tasks in the financial domain.

Improving network security through Moving Target Defense technology.

Investigating Federated Learning for wearable health devices and ontology-based text summarization for efficient information processing.

Awards 🏆

Best Paper Award at the IKT Conference (2021)

Best Researcher Award at the Iran Telecommunication Research Center (ITRC) (2022)

Research Position at the Data Science & Big Data Lab in Seville, Spain (2023)

Nomination for the Gala GSA Award at the University of Saskatchewan (2025).

Selected Publications 📚

C-SHAP: A Hybrid Method for Fast and Efficient Interpretability – Applied Sciences (Q2 Journal), Published 2025.

Leveraging Augmentation Techniques for Tasks with Unbalancedness within the Financial Domain – EPJ Data Science (Q1 Journal), Published 2023.

Investigating Sentiment Analysis of News in Stock Market Prediction – International Journal of Information and Communication Technology Research, Published 2024.

Unsupervised Learning Ontology-Based Text Summarization Approach with Cellular Learning Automata – Journal of Theoretical and Applied Information Technology, Published 2023.

Analyzing the Effect of News Polarity on Stock Market Prediction – Proceedings of the 12th International Conference on Information and Knowledge Technology (IKT), Published 2021.

 

 

Prof. Wen Jiang | Artificial Intelligence | Best Researcher Award

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Prof. Wen Jiang | Artificial Intelligence | Best Researcher Award

Northwestern Polytechnical University, China.

Prof. Wen Jiang is a distinguished researcher and academic with a Ph.D. from Northwestern Polytechnical University, Xi’an, China (2009). She currently serves as a professor in the School of Electronics and Information at Northwestern Polytechnical University. Her work focuses on cutting-edge areas like information fusion, artificial intelligence, remote sensing image processing, and intelligent algorithm security, making her a leader in her field.

Profile

Scopus

Orcid

Education 🎓

Prof. Wen Jiang has an impressive academic background in information systems and engineering. She earned her Ph.D. in Information Systems from Northwestern Polytechnical University, Xi’an, China, in 2009, where her research focused on innovative data systems and intelligent technologies. Prior to that, she completed her Master’s degree in Information Engineering at Information Engineering University, Zhengzhou, China, in 1997, gaining in-depth knowledge of advanced engineering concepts. She began her academic journey with a Bachelor’s degree in Information Engineering from the same university in 1994, building a strong foundation for her pioneering contributions to the field.

Experience 🏫

Prof. Wen Jiang is a Professor at the School of Electronics and Information, Northwestern Polytechnical University, where she has made a significant impact in academia. She is highly regarded for mentoring aspiring researchers and leading innovative projects in advanced technologies. Her leadership and expertise have been instrumental in driving forward research in areas like artificial intelligence, information fusion, and algorithm security..

Research Interests 🔍

Information Fusion:
Integrating data from diverse sources to enable smarter and more efficient decision-making processes, crucial for applications in defense, healthcare, and industry.

Artificial Intelligence:
Advancing machine learning and intelligent systems to solve complex problems and enhance automation across various domains.

Remote Sensing Image Processing:
Developing cutting-edge tools for environmental monitoring, urban planning, disaster management, and mapping applications.

Intelligent Algorithm Security:
Ensuring the robustness, reliability, and safety of AI-driven solutions to address vulnerabilities in critical systems.

Publications Top Notes 📚

A New Data Augmentation Method Based on Mixup and Dempster-Shafer Theory IEEE Transactions on Multimedia, 2024
Contributors: Zhuo Zhang, Hongfei Wang, Jie Geng, Xinyang Deng, Wen Jiang. Link

A Novel Air Target Intention Recognition Method Based on Sample Reweighting and Attention-Bi-GRU IEEE Systems Journal, 2024
Contributors: Yu Zhang, Weichen Ma, Fanghui Huang, Xinyang Deng, Wen Jiang. Link

Causal Intervention and Parameter-Free Reasoning for Few-Shot SAR Target Recognition IEEE Transactions on Circuits and Systems for Video Technology, 2024, Contributors: Jie Geng, Weichen Ma, Wen Jiang. Link

CMSE: Cross-Modal Semantic Enhancement Network for Classification of Hyperspectral and LiDAR Data IEEE Transactions on Geoscience and Remote Sensing, 2024, Contributors: Wenqi Han, Wang Miao, Jie Geng, Wen Jiang. Link

Dual-Path Feature Aware Network for Remote Sensing Image Semantic Segmentation IEEE Transactions on Circuits and Systems for Video Technology, 2024, Contributors: Jie Geng, Shuai Song, Wen Jiang. Link